AWS Certified Machine Learning Engineer – Associate

The AWS Certified Machine Learning Engineer – Associate certification demonstrates expertise in implementing ML workloads and operationalizing them in production. The AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam assesses a candidate’s skills in building, deploying, and maintaining machine learning (ML) solutions and pipelines using AWS Cloud. Further, the exam also tests the candidate’s ability to:

• Ingesting, transforming, validating, and preparing data for ML modeling.
• Selecting modeling techniques, training models, tuning hyperparameters, evaluating model performance, and managing model versions.
• Determining deployment infrastructure, provisioning compute resources, and configuring auto-scaling.
• Setting up CI/CD pipelines to automate ML workflow orchestration.
• Monitoring models, data, and infrastructure for issues.
• Securing ML systems and resources with access controls, compliance, and best practices.

Target Audience

The ideal candidate should have at least one year of experience working with Amazon SageMaker and other AWS services for ML engineering. Additionally, they should have at least one year of experience in a related role, such as a backend software developer, DevOps developer, data engineer, or data scientist.

Recommended General IT Knowledge

The ideal candidate should have the following IT knowledge:

• A basic understanding of common ML algorithms and their applications.
• Fundamentals of data engineering, including familiarity with data formats, ingestion, and transformation for ML data pipelines.
• Skills in querying and transforming data.
• Knowledge of software engineering best practices, such as modular code development, deployment, and debugging.
• Familiarity with provisioning and monitoring both cloud and on-premises ML resources.
• Experience with CI/CD pipelines and infrastructure as code (IaC).
• Proficiency in using code repositories for version control and CI/CD pipelines.

Recommended AWS Knowledge

The ideal candidate should have the following AWS expertise:

• Understanding of SageMaker’s capabilities and algorithms for building and deploying models.
• Knowledge of AWS data storage and processing services to prepare data for modeling.
• Experience with deploying applications and infrastructure on AWS.
• Familiarity with AWS monitoring tools for logging and troubleshooting ML systems.
• Knowledge of AWS services that facilitate automation and orchestration of CI/CD pipelines.
• Understanding of AWS security best practices, including identity and access management, encryption, and data protection.

Exam Details

The AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam is classified as an Associate-level certification. It has a duration of 170 minutes and includes 85 questions. Candidates can take the exam either at a Pearson VUE testing center or through an online proctored option. The exam is available in English and Japanese, with a minimum passing score of 720 on a scaled range of 100 to 1,000.

Question Types

The exam includes the following question formats:

• Multiple Choice: Contains one correct answer and three incorrect options (distractors).
• Multiple Response: Requires selecting two or more correct answers from five or more options. All correct responses must be chosen to earn credit.
• Ordering: Presents a list of 3-5 steps for completing a task. You must select and arrange the steps in the correct sequence.
• Matching: Involves matching a list of responses to 3-7 prompts. All pairs must be matched correctly to earn credit.
• Case Study: Features a single scenario with two or more related questions. Each question is evaluated individually, allowing candidates to earn credit for each correct answer.

Course Outline

This exam guide details the weightings, content domains, and task statements included in the exam. It offers further context for each task statement to support your preparation. The covered topics are:

Domain 1: Data Preparation for Machine Learning (ML)

Task Statement 1.1: Ingest and store data.

Knowledge of:

• Data formats and ingestion mechanisms (for example, validated and non-validated formats, Apache Parquet, JSON, CSV, Apache ORC, Apache Avro, RecordIO)
• How to use the core AWS data sources (for example, Amazon S3, Amazon Elastic File System [Amazon EFS], Amazon FSx for NetApp ONTAP)
• How to use AWS streaming data sources to ingest data (for example, Amazon Kinesis, Apache Flink, Apache Kafka)
• AWS storage options, including use cases and tradeoffs

Skills in:

• Extracting data from storage (for example, Amazon S3, Amazon Elastic Block Store [Amazon EBS], Amazon EFS, Amazon RDS, Amazon DynamoDB) by using relevant AWS service options (for example, Amazon S3 Transfer Acceleration, Amazon EBS Provisioned IOPS)
• Choosing appropriate data formats (for example, Parquet, JSON, CSV, ORC) based on data access patterns
• Ingesting data into Amazon SageMaker Data Wrangler and SageMaker Feature Store
• Merging data from multiple sources (for example, by using programming techniques, AWS Glue, Apache Spark)
• Troubleshooting and debugging data ingestion and storage issues that involve capacity and scalability
• Making initial storage decisions based on cost, performance, and data structure

Task Statement 1.2: Transform data and perform feature engineering.

Knowledge of:

• Data cleaning and transformation techniques (for example, detecting and treating outliers, imputing missing data, combining, deduplication)
• Feature engineering techniques (for example, data scaling and standardization, feature splitting, binning, log transformation, normalization)
• Encoding techniques (for example, one-hot encoding, binary encoding, label encoding, tokenization)
• Tools to explore, visualize, or transform data and features (for example, SageMaker Data Wrangler, AWS Glue, AWS Glue DataBrew)
• Services that transform streaming data (for example, AWS Lambda, Spark)
• Data annotation and labeling services that create high-quality labeled datasets

Skills in:

• Transforming data by using AWS tools (for example, AWS Glue, AWS Glue DataBrew, Spark running on Amazon EMR, SageMaker Data Wrangler)
• Creating and managing features by using AWS tools (for example, SageMaker Feature Store)
• Validating and labeling data by using AWS services (for example, SageMaker Ground Truth, Amazon Mechanical Turk)

Task Statement 1.3: Ensure data integrity and prepare data for modeling.

Knowledge of:

• Pre-training bias metrics for numeric, text, and image data (for example, class imbalance [CI], difference in proportions of labels [DPL])
• Strategies to address CI in numeric, text, and image datasets (for example, synthetic data generation, resampling)
• Techniques to encrypt data
• Data classification, anonymization, and masking
• Implications of compliance requirements (for example, personally identifiable information [PII], protected health information [PHI], data residency)

Skills in:

• Validating data quality (for example, by using AWS Glue DataBrew and AWS Glue Data Quality)
• Identifying and mitigating sources of bias in data (for example, selection bias, measurement bias) by using AWS tools (for example, SageMaker Clarify)
• Preparing data to reduce prediction bias (for example, by using dataset splitting, shuffling, and augmentation)
• Configuring data to load into the model training resource (for example, Amazon EFS, Amazon FSx)

Domain 2: ML Model Development

Task Statement 2.1: Choose a modeling approach.

Knowledge of:

• Capabilities and appropriate uses of ML algorithms to solve business problems
• How to use AWS artificial intelligence (AI) services (for example, Amazon Translate, Amazon Transcribe, Amazon Rekognition, Amazon Bedrock) to solve specific business problems
• How to consider interpretability during model selection or algorithm selection
• SageMaker built-in algorithms and when to apply them

Skills in:

• Assessing available data and problem complexity to determine the feasibility of an ML solution
• Comparing and selecting appropriate ML models or algorithms to solve specific problems
• Choosing built-in algorithms, foundation models, and solution templates (for example, in SageMaker JumpStart and Amazon Bedrock)
• Selecting models or algorithms based on costs
• Selecting AI services to solve common business needs

Task Statement 2.2: Train and refine models.

Knowledge of:

• Elements in the training process (for example, epoch, steps, batch size)
• Methods to reduce model training time (for example, early stopping, distributed training)
• Factors that influence model size
• Methods to improve model performance
• Benefits of regularization techniques (for example, dropout, weight decay, L1 and L2)
• Hyperparameter tuning techniques (for example, random search, Bayesian optimization)
• Model hyperparameters and their effects on model performance (for example, number of trees in a tree-based model, number of layers in a neural network)
• Methods to integrate models that were built outside SageMaker into SageMaker
  

Skills in:

• Using SageMaker built-in algorithms and common ML libraries to develop ML models
• Using SageMaker script mode with SageMaker supported frameworks to train models (for example, TensorFlow, PyTorch)
• Using custom datasets to fine-tune pre-trained models (for example, Amazon Bedrock, SageMaker JumpStart)
• Performing hyperparameter tuning (for example, by using SageMaker automatic model tuning [AMT])
• Integrating automated hyperparameter optimization capabilities
• Preventing model overfitting, underfitting, and catastrophic forgetting (for example, by using regularization techniques, feature selection)
• Combining multiple training models to improve performance (for example, ensembling, stacking, boosting)
• Reducing model size (for example, by altering data types, pruning, updating feature selection, compression)
• Managing model versions for repeatability and audits (for example, by using the SageMaker Model Registry)

Task Statement 2.3: Analyze model performance.

Knowledge of:

• Model evaluation techniques and metrics (for example, confusion matrix, heat maps, F1 score, accuracy, precision, recall, Root Mean Square Error [RMSE], receiver operating characteristic [ROC], Area Under the ROC Curve [AUC])
• Methods to create performance baselines
• Methods to identify model overfitting and underfitting
• Metrics available in SageMaker Clarify to gain insights into ML training data and models
• Convergence issues

Skills in:

• Selecting and interpreting evaluation metrics and detecting model bias
• Assessing tradeoffs between model performance, training time, and cost
• Performing reproducible experiments by using AWS services
• Comparing the performance of a shadow variant to the performance of a production variant
• Using SageMaker Clarify to interpret model outputs
• Using SageMaker Model Debugger to debug model convergence

Domain 3: Deployment and Orchestration of ML Workflows

Task Statement 3.1: Select deployment infrastructure based on existing architecture and requirements.

Knowledge of:

• Deployment best practices (for example, versioning, rollback strategies)
• AWS deployment services (for example, SageMaker)
• Methods to serve ML models in real time and in batches
• How to provision compute resources in production environments and test environments (for example, CPU, GPU)
• Model and endpoint requirements for deployment endpoints (for example, serverless endpoints, real-time endpoints, asynchronous endpoints, batch inference)
• How to choose appropriate containers (for example, provided or customized)
• Methods to optimize models on edge devices (for example, SageMaker Neo)

Skills in:

• Evaluating performance, cost, and latency tradeoffs
• Choosing the appropriate compute environment for training and inference based on requirements (for example, GPU or CPU specifications, processor family, networking bandwidth)
• Selecting the correct deployment orchestrator (for example, Apache Airflow, SageMaker Pipelines)
• Selecting multi-model or multi-container deployments
• Selecting the correct deployment target (for example, SageMaker endpoints, Kubernetes, Amazon Elastic Container Service [Amazon ECS], Amazon Elastic Kubernetes Service [Amazon EKS], Lambda)
• Choosing model deployment strategies (for example, real time, batch)
  

Task Statement 3.2: Create and script infrastructure based on existing architecture and requirements.

Knowledge of:

• Difference between on-demand and provisioned resources
• How to compare scaling policies
• Tradeoffs and use cases of infrastructure as code (IaC) options (for example, AWS CloudFormation, AWS Cloud Development Kit [AWS CDK])
• Containerization concepts and AWS container services
• How to use SageMaker endpoint auto scaling policies to meet scalability requirements (for example, based on demand, time)

Skills in:

• Applying best practices to enable maintainable, scalable, and cost-effective ML solutions (for example, automatic scaling on SageMaker endpoints, dynamically adding Spot Instances, by using Amazon EC2 instances, by using Lambda behind the endpoints)
• Automating the provisioning of compute resources, including communication between stacks (for example, by using CloudFormation, AWS CDK)
• Building and maintaining containers (for example, Amazon Elastic Container Registry [Amazon ECR], Amazon EKS, Amazon ECS, by using bring your own container [BYOC] with SageMaker)
• Configuring SageMaker endpoints within the VPC network
• Deploying and hosting models by using the SageMaker SDK
• Choosing specific metrics for auto scaling (for example, model latency, CPU utilization, invocations per instance)

Task Statement 3.3: Use automated orchestration tools to set up continuous integration and continuous delivery (CI/CD) pipelines.

Knowledge of:

• Capabilities and quotas for AWS CodePipeline, AWS CodeBuild, and AWS CodeDeploy
• Automation and integration of data ingestion with orchestration services
• Version control systems and basic usage (for example, Git)
• CI/CD principles and how they fit into ML workflows
• Deployment strategies and rollback actions (for example, blue/green, canary, linear)
• How code repositories and pipelines work together

Skills in:

• Configuring and troubleshooting CodeBuild, CodeDeploy, and CodePipeline, including stages
• Applying continuous deployment flow structures to invoke pipelines (for example, Gitflow, GitHub Flow)
• Using AWS services to automate orchestration (for example, to deploy ML models, automate model building)
• Configuring training and inference jobs (for example, by using Amazon EventBridge rules, SageMaker Pipelines, CodePipeline)
• Creating automated tests in CI/CD pipelines (for example, integration tests, unit tests, end-to-end tests)
• Building and integrating mechanisms to retrain models

Domain 4: ML Solution Monitoring, Maintenance, and Security

Task Statement 4.1: Monitor model inference.

Knowledge of:

• Drift in ML models
• Techniques to monitor data quality and model performance
• Design principles for ML lenses relevant to monitoring

Skills in:

• Monitoring models in production (for example, by using SageMaker Model Monitor)
• Monitoring workflows to detect anomalies or errors in data processing or model inference
• Detecting changes in the distribution of data that can affect model performance (for example, by using SageMaker Clarify)
• Monitoring model performance in production by using A/B testing

Task Statement 4.2: Monitor and optimize infrastructure and costs.

Knowledge of:

• Key performance metrics for ML infrastructure (for example, utilization, throughput, availability, scalability, fault tolerance)
• Monitoring and observability tools to troubleshoot latency and performance issues (for example, AWS X-Ray, Amazon CloudWatch Lambda Insights, Amazon CloudWatch Logs Insights)
• How to use AWS CloudTrail to log, monitor, and invoke re-training activities
• Differences between instance types and how they affect performance (for example, memory optimized, compute optimized, general purpose, inference optimized)
• Capabilities of cost analysis tools (for example, AWS Cost Explorer, AWS Billing and Cost Management, AWS Trusted Advisor)
• Cost tracking and allocation techniques (for example, resource tagging)

Skills in:

• Configuring and using tools to troubleshoot and analyze resources (for example, CloudWatch Logs, CloudWatch alarms)
• Creating CloudTrail trails
• Setting up dashboards to monitor performance metrics (for example, by using Amazon QuickSight, CloudWatch dashboards)
• Monitoring infrastructure (for example, by using EventBridge events)
• Rightsizing instance families and sizes (for example, by using SageMaker Inference Recommender and AWS Compute Optimizer)
• Monitoring and resolving latency and scaling issues
• Preparing infrastructure for cost monitoring (for example, by applying a tagging strategy)
• Troubleshooting capacity concerns that involve cost and performance (for example, provisioned concurrency, service quotas, auto scaling)
• Optimizing costs and setting cost quotas by using appropriate cost management tools (for example, AWS Cost Explorer, AWS Trusted Advisor, AWS Budgets)
• Optimizing infrastructure costs by selecting purchasing options (for example, Spot Instances, On-Demand Instances, Reserved Instances, SageMaker Savings Plans)

Task Statement 4.3: Secure AWS resources.

Knowledge of:

• IAM roles, policies, and groups that control access to AWS services (for example, AWS Identity and Access Management [IAM], bucket policies, SageMaker Role Manager)
• SageMaker security and compliance features
• Controls for network access to ML resources
• Security best practices for CI/CD pipelines

Skills in:

• Configuring least privilege access to ML artifacts
• Configuring IAM policies and roles for users and applications that interact with ML systems
• Monitoring, auditing, and logging ML systems to ensure continued security and compliance
• Troubleshooting and debugging security issues
• Building VPCs, subnets, and security groups to securely isolate ML systems

AWS Certified Machine Learning Engineer – Associate: FAQs

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AWS Exam Policy

Amazon Web Services (AWS) establishes clear rules and procedures for their certification exams. These guidelines address multiple facets of exam preparation and certification. Some of the key policies include:

Retake Policy

If you do not pass an exam, you must wait 14 calendar days before you can retake it. There is no limit on the number of attempts, but you will need to pay the full registration fee for each try. After passing an exam, you cannot retake the same exam for two years. However, if the exam has been updated with a new exam guide and exam series code, you will be eligible to take the updated version.

Exam Results

The AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam is designated as either pass or fail. Scoring is based on a minimum standard set by AWS professionals who adhere to certification industry best practices and guidelines. Your exam results are presented as a scaled score ranging from 100 to 1,000, with a minimum passing score of 720. This score reflects your overall performance on the exam and indicates whether you passed. Scaled scoring models are used to standardize scores across various exam forms that may vary in difficulty. Your score report may include a table that classifies your performance in each section. The exam employs a compensatory scoring model, meaning you do not need to achieve a passing score in every section; you only need to pass the overall exam.

AWS Certified Machine Learning Engineer – Associate Exam Study Guide

1. Understand the Exam Guide

Using the AWS Certified Machine Learning Engineer – Associate Exam guide is crucial for effective exam preparation. This guide provides a detailed overview of the exam structure, including the weightings for different content domains and specific task statements. By reviewing these sections, candidates can pinpoint key focus areas and adjust their study time accordingly.

Furthermore, the guide offers insights into the types of questions that may be included in the exam, allowing candidates to become familiar with the format and refine their test-taking strategies. Utilizing this resource can significantly improve your understanding of AI and machine learning concepts as they apply to AWS, ultimately increasing your confidence and readiness for the certification exam.

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4. Join Study Groups

Joining study groups offers a dynamic and collaborative way to prepare for the AWS Certified Machine Learning Engineer – Associate exam. By participating in these groups, you connect with a community of individuals who are also navigating the complexities of AWS certifications. Engaging in discussions, sharing experiences, and addressing challenges together can provide valuable insights and deepen your understanding of key concepts.

Study groups create a supportive environment where members can clarify doubts, exchange tips, and stay motivated throughout their certification journey. This collaborative learning experience not only strengthens your grasp of AWS technologies but also fosters a sense of camaraderie among peers with similar goals.

5. Use Practice Tests

Incorporating practice tests into your study strategy for the AWS Certified Machine Learning Engineer – Associate exam is essential for success. These practice tests mimic the actual exam environment, allowing you to assess your knowledge, identify areas for improvement, and familiarize yourself with the types of questions you may encounter.

Regularly taking practice tests boosts your confidence, sharpens your time-management skills, and ensures you are well-prepared for the unique challenges of AWS certification exams. By blending the advantages of study groups with practice tests, you develop a comprehensive and effective approach to mastering AWS technologies and earning your certification.